// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2016
// Mehdi Goli    Codeplay Software Ltd.
// Ralph Potter  Codeplay Software Ltd.
// Luke Iwanski  Codeplay Software Ltd.
// Contact: <eigen@codeplay.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.

#define EIGEN_TEST_NO_LONGDOUBLE
#define EIGEN_TEST_NO_COMPLEX

#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
#define EIGEN_USE_SYCL
#define EIGEN_HAS_CONSTEXPR 1

#include "main.h"

#include <unsupported/Eigen/CXX11/Tensor>

using Eigen::array;
using Eigen::SyclDevice;
using Eigen::Tensor;
using Eigen::TensorMap;

template<typename DataType, int Layout, typename DenseIndex>
static void
test_sycl_simple_argmax(const Eigen::SyclDevice& sycl_device)
{
	Tensor<DataType, 3, Layout, DenseIndex> in(Eigen::array<DenseIndex, 3>{ { 2, 2, 2 } });
	Tensor<DenseIndex, 0, Layout, DenseIndex> out_max;
	Tensor<DenseIndex, 0, Layout, DenseIndex> out_min;
	in.setRandom();
	in *= in.constant(100.0);
	in(0, 0, 0) = -1000.0;
	in(1, 1, 1) = 1000.0;

	std::size_t in_bytes = in.size() * sizeof(DataType);
	std::size_t out_bytes = out_max.size() * sizeof(DenseIndex);

	DataType* d_in = static_cast<DataType*>(sycl_device.allocate(in_bytes));
	DenseIndex* d_out_max = static_cast<DenseIndex*>(sycl_device.allocate(out_bytes));
	DenseIndex* d_out_min = static_cast<DenseIndex*>(sycl_device.allocate(out_bytes));

	Eigen::TensorMap<Eigen::Tensor<DataType, 3, Layout, DenseIndex>> gpu_in(d_in,
																			Eigen::array<DenseIndex, 3>{ { 2, 2, 2 } });
	Eigen::TensorMap<Eigen::Tensor<DenseIndex, 0, Layout, DenseIndex>> gpu_out_max(d_out_max);
	Eigen::TensorMap<Eigen::Tensor<DenseIndex, 0, Layout, DenseIndex>> gpu_out_min(d_out_min);
	sycl_device.memcpyHostToDevice(d_in, in.data(), in_bytes);

	gpu_out_max.device(sycl_device) = gpu_in.argmax();
	gpu_out_min.device(sycl_device) = gpu_in.argmin();

	sycl_device.memcpyDeviceToHost(out_max.data(), d_out_max, out_bytes);
	sycl_device.memcpyDeviceToHost(out_min.data(), d_out_min, out_bytes);

	VERIFY_IS_EQUAL(out_max(), 2 * 2 * 2 - 1);
	VERIFY_IS_EQUAL(out_min(), 0);

	sycl_device.deallocate(d_in);
	sycl_device.deallocate(d_out_max);
	sycl_device.deallocate(d_out_min);
}

template<typename DataType, int DataLayout, typename DenseIndex>
static void
test_sycl_argmax_dim(const Eigen::SyclDevice& sycl_device)
{
	DenseIndex sizeDim0 = 9;
	DenseIndex sizeDim1 = 3;
	DenseIndex sizeDim2 = 5;
	DenseIndex sizeDim3 = 7;
	Tensor<DataType, 4, DataLayout, DenseIndex> tensor(sizeDim0, sizeDim1, sizeDim2, sizeDim3);

	std::vector<DenseIndex> dims;
	dims.push_back(sizeDim0);
	dims.push_back(sizeDim1);
	dims.push_back(sizeDim2);
	dims.push_back(sizeDim3);
	for (DenseIndex dim = 0; dim < 4; ++dim) {
		array<DenseIndex, 3> out_shape;
		for (DenseIndex d = 0; d < 3; ++d)
			out_shape[d] = (d < dim) ? dims[d] : dims[d + 1];

		Tensor<DenseIndex, 3, DataLayout, DenseIndex> tensor_arg(out_shape);

		array<DenseIndex, 4> ix;
		for (DenseIndex i = 0; i < sizeDim0; ++i) {
			for (DenseIndex j = 0; j < sizeDim1; ++j) {
				for (DenseIndex k = 0; k < sizeDim2; ++k) {
					for (DenseIndex l = 0; l < sizeDim3; ++l) {
						ix[0] = i;
						ix[1] = j;
						ix[2] = k;
						ix[3] = l;
						// suppose dim == 1, then for all i, k, l, set tensor(i, 0, k, l)
						// = 10.0
						tensor(ix) = (ix[dim] != 0) ? -1.0 : 10.0;
					}
				}
			}
		}

		std::size_t in_bytes = tensor.size() * sizeof(DataType);
		std::size_t out_bytes = tensor_arg.size() * sizeof(DenseIndex);

		DataType* d_in = static_cast<DataType*>(sycl_device.allocate(in_bytes));
		DenseIndex* d_out = static_cast<DenseIndex*>(sycl_device.allocate(out_bytes));

		Eigen::TensorMap<Eigen::Tensor<DataType, 4, DataLayout, DenseIndex>> gpu_in(
			d_in, Eigen::array<DenseIndex, 4>{ { sizeDim0, sizeDim1, sizeDim2, sizeDim3 } });
		Eigen::TensorMap<Eigen::Tensor<DenseIndex, 3, DataLayout, DenseIndex>> gpu_out(d_out, out_shape);

		sycl_device.memcpyHostToDevice(d_in, tensor.data(), in_bytes);
		gpu_out.device(sycl_device) = gpu_in.argmax(dim);
		sycl_device.memcpyDeviceToHost(tensor_arg.data(), d_out, out_bytes);

		VERIFY_IS_EQUAL(static_cast<size_t>(tensor_arg.size()),
						size_t(sizeDim0 * sizeDim1 * sizeDim2 * sizeDim3 / tensor.dimension(dim)));

		for (DenseIndex n = 0; n < tensor_arg.size(); ++n) {
			// Expect max to be in the first index of the reduced dimension
			VERIFY_IS_EQUAL(tensor_arg.data()[n], 0);
		}

		sycl_device.synchronize();

		for (DenseIndex i = 0; i < sizeDim0; ++i) {
			for (DenseIndex j = 0; j < sizeDim1; ++j) {
				for (DenseIndex k = 0; k < sizeDim2; ++k) {
					for (DenseIndex l = 0; l < sizeDim3; ++l) {
						ix[0] = i;
						ix[1] = j;
						ix[2] = k;
						ix[3] = l;
						// suppose dim == 1, then for all i, k, l, set tensor(i, 2, k, l) = 20.0
						tensor(ix) = (ix[dim] != tensor.dimension(dim) - 1) ? -1.0 : 20.0;
					}
				}
			}
		}

		sycl_device.memcpyHostToDevice(d_in, tensor.data(), in_bytes);
		gpu_out.device(sycl_device) = gpu_in.argmax(dim);
		sycl_device.memcpyDeviceToHost(tensor_arg.data(), d_out, out_bytes);

		for (DenseIndex n = 0; n < tensor_arg.size(); ++n) {
			// Expect max to be in the last index of the reduced dimension
			VERIFY_IS_EQUAL(tensor_arg.data()[n], tensor.dimension(dim) - 1);
		}
		sycl_device.deallocate(d_in);
		sycl_device.deallocate(d_out);
	}
}

template<typename DataType, int DataLayout, typename DenseIndex>
static void
test_sycl_argmin_dim(const Eigen::SyclDevice& sycl_device)
{
	DenseIndex sizeDim0 = 9;
	DenseIndex sizeDim1 = 3;
	DenseIndex sizeDim2 = 5;
	DenseIndex sizeDim3 = 7;
	Tensor<DataType, 4, DataLayout, DenseIndex> tensor(sizeDim0, sizeDim1, sizeDim2, sizeDim3);

	std::vector<DenseIndex> dims;
	dims.push_back(sizeDim0);
	dims.push_back(sizeDim1);
	dims.push_back(sizeDim2);
	dims.push_back(sizeDim3);
	for (DenseIndex dim = 0; dim < 4; ++dim) {
		array<DenseIndex, 3> out_shape;
		for (DenseIndex d = 0; d < 3; ++d)
			out_shape[d] = (d < dim) ? dims[d] : dims[d + 1];

		Tensor<DenseIndex, 3, DataLayout, DenseIndex> tensor_arg(out_shape);

		array<DenseIndex, 4> ix;
		for (DenseIndex i = 0; i < sizeDim0; ++i) {
			for (DenseIndex j = 0; j < sizeDim1; ++j) {
				for (DenseIndex k = 0; k < sizeDim2; ++k) {
					for (DenseIndex l = 0; l < sizeDim3; ++l) {
						ix[0] = i;
						ix[1] = j;
						ix[2] = k;
						ix[3] = l;
						// suppose dim == 1, then for all i, k, l, set tensor(i, 0, k, l) = -10.0
						tensor(ix) = (ix[dim] != 0) ? 1.0 : -10.0;
					}
				}
			}
		}

		std::size_t in_bytes = tensor.size() * sizeof(DataType);
		std::size_t out_bytes = tensor_arg.size() * sizeof(DenseIndex);

		DataType* d_in = static_cast<DataType*>(sycl_device.allocate(in_bytes));
		DenseIndex* d_out = static_cast<DenseIndex*>(sycl_device.allocate(out_bytes));

		Eigen::TensorMap<Eigen::Tensor<DataType, 4, DataLayout, DenseIndex>> gpu_in(
			d_in, Eigen::array<DenseIndex, 4>{ { sizeDim0, sizeDim1, sizeDim2, sizeDim3 } });
		Eigen::TensorMap<Eigen::Tensor<DenseIndex, 3, DataLayout, DenseIndex>> gpu_out(d_out, out_shape);

		sycl_device.memcpyHostToDevice(d_in, tensor.data(), in_bytes);
		gpu_out.device(sycl_device) = gpu_in.argmin(dim);
		sycl_device.memcpyDeviceToHost(tensor_arg.data(), d_out, out_bytes);

		VERIFY_IS_EQUAL(static_cast<size_t>(tensor_arg.size()),
						size_t(sizeDim0 * sizeDim1 * sizeDim2 * sizeDim3 / tensor.dimension(dim)));

		for (DenseIndex n = 0; n < tensor_arg.size(); ++n) {
			// Expect max to be in the first index of the reduced dimension
			VERIFY_IS_EQUAL(tensor_arg.data()[n], 0);
		}

		sycl_device.synchronize();

		for (DenseIndex i = 0; i < sizeDim0; ++i) {
			for (DenseIndex j = 0; j < sizeDim1; ++j) {
				for (DenseIndex k = 0; k < sizeDim2; ++k) {
					for (DenseIndex l = 0; l < sizeDim3; ++l) {
						ix[0] = i;
						ix[1] = j;
						ix[2] = k;
						ix[3] = l;
						// suppose dim == 1, then for all i, k, l, set tensor(i, 2, k, l) = -20.0
						tensor(ix) = (ix[dim] != tensor.dimension(dim) - 1) ? 1.0 : -20.0;
					}
				}
			}
		}

		sycl_device.memcpyHostToDevice(d_in, tensor.data(), in_bytes);
		gpu_out.device(sycl_device) = gpu_in.argmin(dim);
		sycl_device.memcpyDeviceToHost(tensor_arg.data(), d_out, out_bytes);

		for (DenseIndex n = 0; n < tensor_arg.size(); ++n) {
			// Expect max to be in the last index of the reduced dimension
			VERIFY_IS_EQUAL(tensor_arg.data()[n], tensor.dimension(dim) - 1);
		}
		sycl_device.deallocate(d_in);
		sycl_device.deallocate(d_out);
	}
}

template<typename DataType, typename Device_Selector>
void
sycl_argmax_test_per_device(const Device_Selector& d)
{
	QueueInterface queueInterface(d);
	auto sycl_device = Eigen::SyclDevice(&queueInterface);
	test_sycl_simple_argmax<DataType, RowMajor, int64_t>(sycl_device);
	test_sycl_simple_argmax<DataType, ColMajor, int64_t>(sycl_device);
	test_sycl_argmax_dim<DataType, ColMajor, int64_t>(sycl_device);
	test_sycl_argmax_dim<DataType, RowMajor, int64_t>(sycl_device);
	test_sycl_argmin_dim<DataType, ColMajor, int64_t>(sycl_device);
	test_sycl_argmin_dim<DataType, RowMajor, int64_t>(sycl_device);
}

EIGEN_DECLARE_TEST(cxx11_tensor_argmax_sycl)
{
	for (const auto& device : Eigen::get_sycl_supported_devices()) {
		CALL_SUBTEST(sycl_argmax_test_per_device<float>(device));
	}
}
